A tactile sensing surface for artificial neural network based automatic recognition of the contact force position

The paper describes a new technique for data entry hardware. It is a high-l evel interpretation method based on the back-propagation neural network mod el, in which two aspects of research and development works are illustrated in detail: one is a tactile sensing surface comprising a deformable surface with optical sensing elements; the other is the data acquisition and proce ssing model in which a neural network model, called Tactile Position Recogn ition, is programmed to realize the real-time and precise recognition of a contact force position, which enables the contact position of a constant fo rce to be determined within an accuracy less than 4 per cent of full scale in a continuous spatial resolution of 35 zones. The device described utilizes a simple low-cost and robust mechanical desig n combined with software to interpret sensory data to measure the contact p osition of a normal force applied on a planar surface. The high-level inter pretation method for this system enables automatic determination of contact position in real time.